from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from dotenv import load_dotenv
from langgraph.prebuilt import ToolNode
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver  # Changed to AsyncSqliteSaver for async support
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
from typing import List, Any, Optional, Dict
from pydantic import BaseModel, Field
from sidekick_tools import playwright_tools, other_tools
import uuid
import asyncio
import aiosqlite
from datetime import datetime

load_dotenv(override=True)


class State(TypedDict):
    messages: Annotated[List[Any], add_messages]
    success_criteria: str
    feedback_on_work: Optional[str]
    success_criteria_met: bool
    user_input_needed: bool


class EvaluatorOutput(BaseModel):
    feedback: str = Field(description="Feedback on the assistant's response")
    success_criteria_met: bool = Field(description="Whether the success criteria have been met")
    user_input_needed: bool = Field(
        description="True if more input is needed from the user, or clarifications, or the assistant is stuck"
    )


class Sidekick:
    def __init__(self, username: str = None):
        """
        Initialize Sidekick with optional username for SQLite memory persistence.
        
        Args:
            username: Username to use as thread_id for persistent memory. 
                     If None, generates a random UUID (non-persistent session).
        """
        self.worker_llm_with_tools = None
        self.evaluator_llm_with_output = None
        self.tools = None
        self.llm_with_tools = None
        self.graph = None
        self.sidekick_id = username if username else str(uuid.uuid4())
        self.db_path = "sidekick_memory.db"
        self.memory = None  
        self.browser = None
        self.playwright = None

    async def setup(self):
        self.db_conn = await aiosqlite.connect(self.db_path)
        self.memory = AsyncSqliteSaver(self.db_conn)
        self.tools, self.browser, self.playwright = await playwright_tools()
        self.tools += await other_tools()
        worker_llm = ChatOpenAI(model="gpt-4o-mini")
        self.worker_llm_with_tools = worker_llm.bind_tools(self.tools)
        evaluator_llm = ChatOpenAI(model="gpt-4o-mini")
        self.evaluator_llm_with_output = evaluator_llm.with_structured_output(EvaluatorOutput)
        await self.build_graph()

    def worker(self, state: State) -> Dict[str, Any]:
        system_message = f"""You are a helpful assistant that can use tools to complete tasks.
    You keep working on a task until either you have a question or clarification for the user, or the success criteria is met.
    You have many tools to help you, including tools to browse the internet, navigating and retrieving web pages.
    You have a tool to run python code, but note that you would need to include a print() statement if you wanted to receive output.
    If you need to download a file from the internet, you should use your python code using the requests package. You can later move this file using your file management tools.
    You should use your code_review tool only once to improve the code given or generated. Do not use it multiple times.
    The current date and time is {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}

    This is the success criteria:
    {state["success_criteria"]}
    You should reply either with a question for the user about this assignment, or with your final response.
    If you have a question for the user, you need to reply by clearly stating your question. An example might be:

    Question: please clarify whether you want a summary or a detailed answer

    If you've finished, reply with the final answer, and don't ask a question; simply reply with the answer.
    """

        if state.get("feedback_on_work"):
            system_message += f"""
    Previously you thought you completed the assignment, but your reply was rejected because the success criteria was not met.
    Here is the feedback on why this was rejected:
    {state["feedback_on_work"]}
    With this feedback, please continue the assignment, ensuring that you meet the success criteria or have a question for the user."""

        # Add in the system message

        found_system_message = False
        messages = state["messages"]
        for message in messages:
            if isinstance(message, SystemMessage):
                message.content = system_message
                found_system_message = True

        if not found_system_message:
            messages = [SystemMessage(content=system_message)] + messages

        # Invoke the LLM with tools
        response = self.worker_llm_with_tools.invoke(messages)

        # Return updated state
        return {
            "messages": [response],
        }

    def worker_router(self, state: State) -> str:
        last_message = state["messages"][-1]

        if hasattr(last_message, "tool_calls") and last_message.tool_calls:
            return "tools"
        else:
            return "evaluator"

    def format_conversation(self, messages: List[Any]) -> str:
        conversation = "Conversation history:\n\n"
        for message in messages:
            if isinstance(message, HumanMessage):
                conversation += f"User: {message.content}\n"
            elif isinstance(message, AIMessage):
                text = message.content or "[Tools use]"
                conversation += f"Assistant: {text}\n"
        return conversation

    def evaluator(self, state: State) -> State:
        last_response = state["messages"][-1].content

        system_message = """You are an evaluator that determines if a task has been completed successfully by an Assistant.
    Assess the Assistant's last response based on the given criteria. Respond with your feedback, and with your decision on whether the success criteria has been met,
    and whether more input is needed from the user."""

        user_message = f"""You are evaluating a conversation between the User and Assistant. You decide what action to take based on the last response from the Assistant.

    The entire conversation with the assistant, with the user's original request and all replies, is:
    {self.format_conversation(state["messages"])}

    The success criteria for this assignment is:
    {state["success_criteria"]}

    And the final response from the Assistant that you are evaluating is:
    {last_response}

    Respond with your feedback, and decide if the success criteria is met by this response.
    Also, decide if more user input is required, either because the assistant has a question, needs clarification, or seems to be stuck and unable to answer without help.

    The Assistant has access to a tool to write files. If the Assistant says they have written a file, then you can assume they have done so.
    Overall you should give the Assistant the benefit of the doubt if they say they've done something. But you should reject if you feel that more work should go into this.

    """
        if state["feedback_on_work"]:
            user_message += f"Also, note that in a prior attempt from the Assistant, you provided this feedback: {state['feedback_on_work']}\n"
            user_message += "If you're seeing the Assistant repeating the same mistakes, then consider responding that user input is required."

        evaluator_messages = [
            SystemMessage(content=system_message),
            HumanMessage(content=user_message),
        ]

        eval_result = self.evaluator_llm_with_output.invoke(evaluator_messages)
        new_state = {
            "messages": [
                {
                    "role": "assistant",
                    "content": f"Evaluator Feedback on this answer: {eval_result.feedback}",
                }
            ],
            "feedback_on_work": eval_result.feedback,
            "success_criteria_met": eval_result.success_criteria_met,
            "user_input_needed": eval_result.user_input_needed,
        }
        return new_state

    def route_based_on_evaluation(self, state: State) -> str:
        if state["success_criteria_met"] or state["user_input_needed"]:
            return "END"
        else:
            return "worker"

    async def build_graph(self):
        # Set up Graph Builder with State
        graph_builder = StateGraph(State)

        # Add nodes
        graph_builder.add_node("worker", self.worker)
        graph_builder.add_node("tools", ToolNode(tools=self.tools))
        graph_builder.add_node("evaluator", self.evaluator)

        # Add edges
        graph_builder.add_conditional_edges(
            "worker", self.worker_router, {"tools": "tools", "evaluator": "evaluator"}
        )
        graph_builder.add_edge("tools", "worker")
        graph_builder.add_conditional_edges(
            "evaluator", self.route_based_on_evaluation, {"worker": "worker", "END": END}
        )
        graph_builder.add_edge(START, "worker")

        # Compile the graph
        self.graph = graph_builder.compile(checkpointer=self.memory)

    async def run_superstep(self, message, success_criteria, history):
        config = {
            "configurable": {"thread_id": self.sidekick_id},
            "recursion_limit": 10
        }

        state = {
            "messages": message,
            "success_criteria": success_criteria or "The answer should be clear and accurate",
            "feedback_on_work": None,
            "success_criteria_met": False,
            "user_input_needed": False,
        }
        result = await self.graph.ainvoke(state, config=config)
        user = {"role": "user", "content": message}
        reply = {"role": "assistant", "content": result["messages"][-2].content}
        feedback = {"role": "assistant", "content": result["messages"][-1].content}
        return history + [user, reply, feedback]

    async def get_history(self):
        """Retrieve conversation history from current state."""
        config = {"configurable": {"thread_id": self.sidekick_id}}
        
        try:
            # Get the current state for this thread
            state = await self.graph.aget_state(config)
            messages = state.values.get("messages", [])
            
            # Convert messages to Gradio chat format
            history_messages = []
            for msg in messages:
                if isinstance(msg, HumanMessage):
                    history_messages.append({"role": "user", "content": msg.content})
                elif isinstance(msg, AIMessage):
                    if msg.content:  # Only add if there's actual content
                        history_messages.append({"role": "assistant", "content": msg.content})
            
            return history_messages
        except Exception as e:
            print(f"Error retrieving history: {e}")
            return [{"role": "assistant", "content": f"Error loading history: {str(e)}"}]

    async def clear_history(self):
        """Clear conversation history from database for this thread."""
        try:
            # Delete checkpoints for this thread_id from the database
            async with self.db_conn.execute(
                "DELETE FROM checkpoints WHERE thread_id = ?", 
                (self.sidekick_id,)
            ) as cursor:
                await self.db_conn.commit()
                rows_deleted = cursor.rowcount
            
            # Also clear from checkpoint_writes table if it exists
            try:
                async with self.db_conn.execute(
                    "DELETE FROM checkpoint_writes WHERE thread_id = ?", 
                    (self.sidekick_id,)
                ):
                    await self.db_conn.commit()
            except:
                pass  # Table might not exist in all versions
            
            return f"History cleared successfully! ({rows_deleted} checkpoints removed)"
        except Exception as e:
            return f"Error clearing history: {str(e)}"

    def cleanup(self):
        """Clean up resources including browser, playwright, and database connection."""
        try:
            loop = asyncio.get_running_loop()
            # Close browser
            if self.browser:
                loop.create_task(self.browser.close())
            # Stop playwright
            if self.playwright:
                loop.create_task(self.playwright.stop())
            # Close database connection
            if hasattr(self, 'db_conn') and self.db_conn:
                loop.create_task(self.db_conn.close())
        except RuntimeError:
            # If no loop is running, do a direct run
            if self.browser:
                asyncio.run(self.browser.close())
            if self.playwright:
                asyncio.run(self.playwright.stop())
            if hasattr(self, 'db_conn') and self.db_conn:
                asyncio.run(self.db_conn.close())
